PromptQL
What is PromptQL?
PromptQL is a multiplayer AI platform for teams that combines shared AI threads, connected business data, collaborative corrections, and a self-organizing team wiki. It works like Claude or ChatGPT with shared context, allowing teammates to review answers, tag experts, preserve reasoning, and turn corrections into reusable skills and knowledge. PromptQL connects sources such as Slack, documents, CRM systems, SaaS applications, data warehouses, coding agents, and product analytics while providing scoped permissions and audit controls.
How to use PromptQL?
Create a shared AI thread and ask PromptQL to analyze connected business data, documents, messages, or applications. Review the sources, assumptions, and results it provides. Tag teammates to verify or correct the answer when needed. PromptQL can then suggest the correction as a wiki, skill, or semantic-layer update; review and approve it so the shared context is available to the team in future threads. Choose models per thread, monitor OLU usage, and configure permissions, quotas, and spending limits as needed.
PromptQL's Core Features
Multiplayer AI threads with shared team context
Collaborative answer review, correction, and refinement
Automatically suggested wiki and knowledge-base updates
Connections to Slack, Google Docs, CRM systems, SaaS applications, data warehouses, and analytics tools
Source citations, visible assumptions, and step-by-step work
Reusable team skills and semantic-layer knowledge
Wikipedia-like pages with revision history and editorial controls
Scoped access controls and granular multi-user permissions
Data-access audit trails
External, confidential, and personal context separation
Per-thread model selection across proprietary and open-weight models
OLU-based usage tracking with per-step consumption details
Per-user quotas, spending alerts, prepaid billing, and auto-pause controls
Enterprise security features including SSO, private networking, dedicated VPC, and BYOC
PromptQL's Use Cases
Analyze customer usage, support tickets, and CRM data to assess churn risk
Create shared account knowledge by combining Slack, call transcripts, product analytics, warehouse data, and CRM records
Detect and correct stale data sources used in financial or board reports
Build reusable revenue, customer-success, and operational analysis skills
Run AI analyst workflows for financial services, healthcare, and retail
Research business questions across connected SaaS applications and databases
Maintain internal playbooks, semantic models, and customer-facing knowledge with controlled access
Collaborate on technical investigations involving coding agents, cloud tools, monitoring systems, and project-management platforms
FAQ from PromptQL
What is PromptQL?
How does PromptQL capture team knowledge?
What data sources can PromptQL connect to?
What is an OLU?
How much does PromptQL cost?
Is there a free plan or free trial?
Can I use my own OpenAI Codex plan?
Can PromptQL control access to sensitive information?
What happens when free credits or prepaid balance run out?
PromptQL Reviews (0)
PromptQL Pricing
Starter
$0.20 per OLU
Introductory at-cost pricing is $0.14 per OLU. Includes up to $100 in project credits, up to $70 for each teammate who joins, no minimum usage, prepaid billing, per-user quotas, and auto-pause at zero balance. Usage on a connected personal OpenAI Codex plan costs $0 per OLU.
Enterprise
Custom pricing
Includes Starter features plus advanced security, fine-grained permissions, data-access audit trails, dedicated VPC or BYOC, private networking and VPC peering, SSO, bring-your-own LLMs, and forward-deployed engineering support.
Analytic of PromptQL
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